Integrating Large Language Models in .NET Projects
The integration of Artificial Intelligence (AI) into applications has become a game-changer. It opens doors to smarter solutions. It also leads to more efficient processes and richer user experiences. AI capabilities help applications to become more “experienced” within their provided solutions. I believe developing AI-supported applications is much more important and critical now. This importance has increased significantly compared to 1-2 years ago.
Nowadays, one of the most important and popular competencies of AI is large language models (LLM). LLM’s provides a valuable way to interact with applications, also, they help applications to interact with end-users easier.
In this post, as a developer, I’ll walk you through building an application with some AI capabilities within .NET Platform. .NET Platform offers a powerful and versatile APIs to build AI-supported applications. It enables seamless incorporation of large language models (LLM) within new APIs. I will try to show some ways of interacting with LLMs and querying custom data within LLMs (a.k.a Retrieval-Augmented Generation) so that you have some idea for starting point.
RAG combines data retrieval with AI’s generative capacities, providing more comprehensive and contextually aware responses. Within RAG, LLMs find most relevant data for a given prompt fetched from custom data source (database, documents…etc.) and generates detailed and related response. It’s particularly useful when dealing with complex questions where the AI might need to refer to specific pieces of information, like answering questions about a certain topic or providing insights from a set of documents. This approach helps make AI models smarter and more reliable by ensuring their responses are both informative and grounded in actual data.
